Chih-Tien Fan

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Recently, depression has becomes a widespread disease throughout the world. However, most people are not aware of the possibility of becoming depressed during their daily lives. Therefore, obtaining an accurate diagnosis of depression is an important issue in healthcare. In this study, we built an inference model based on an ontology and a Bayesian network to infer the possibility of becoming depressed, and we implemented a prototype using a mobile agent platform as a proof-of-concept in the mobile cloud. We developed an ontology model based on the terminology used to describe depression and we utilized a Bayesian network to infer the probability of becoming depressed. We also implemented the system using multi-agents to run on the Android platform, thereby demonstrating the feasibility of this method, and we addressed various implementation issues. The results showed that our method may be useful for inferring a diagnosis of depression. Integrating Ontology with Bayesian Network to predict getting depressed or not.Using mobile agent and cloud environment to implement the diagnosis environment.Evaluation result shown the system is feasibility for doing the prediction.

Agent-Based Service Migration Framework in Hybrid Cloud

With the advance of cloud computing, hybrid cloud that integrate private and public cloud is increasingly becoming an important research issue. Migrating cloud applications from a busy host to an idle host needs an efficient way to guarantee the performance in the geographical heterogeneous cloud environment. This paper we propose an automatic, intelligent service migration framework on a hybrid cloud based on agent technology. We build a prototype that integrated our private cloud with public cloud. In the prototype, mobile agent technique is exploited to manage all resources, monitor system behaviour, and negotiate all actions in the hybrid cloud, in order to achieve automatic, intelligent service migration between the clouds. We demonstrate the service migration mechanism on Hadoop platform between our platform and ITRI¡¦s public cloud.

Agent-Based Intelligent Software Exploits Near-Field Communication

Using a mobile device with embedded near-field communication, an agent-based software intelligence framework can make various applications in a pervasive computing environment more personalized, dynamic, and intelligent.

Execution Time Prediction Using Rough Set Theory in Hybrid Cloud

Execution time prediction is an important issue in cloud computing. Predicting the execution time fast and accurately not only can help users to schedule jobs smarter, but also maximize the throughput and minimize the resource consumption of cloud platform. While hybrid cloud provides methods to federate multiple cloud platforms, different cloud platforms have different resource attributes, which will increase the difficulties to predict a jobs execution time. In this paper, we exploit Rough Set Theory (RST), which is a well-known prediction technique that uses the historical data, to predict the execution time of jobs. The evaluation presents that RST can utilize the accuracy of the execution time, while the decision can be made in a short period of time.

Supporting software intelligence in ubiquitous environment exploits mobile agent

Mobile agent (MA) is a popular approach being applied to numerous applications to achieve predefined goal. A ubiquitous computing engages many computational devices and systems simultaneously, and may not necessarily even be aware that they are doing so. The context of MA, including traveling path and manipulation method, may be dynamically changed according to current state of environment. Therefore, a flexible framework that can handle various MAs for a variety of applications in a ubiquitous environment is necessary. In this paper, we present an integrated and flexible framework that can adopt various applications in which the functionality of mobile agent is distinguishable in diversity of networks or systems. We design and implement an agent-based platform that can guide various MAs that are initiated by mobile devices to complete their goal. In addition, we also conduct three experiments to evaluate the performance of MAG system. The evaluation result shows that the latency is reasonable and acceptable.

Web Resource Cacheable Edge Device in Fog Computing

The bandwidth of the network is precious. As cloud computing provides powerful computation power and more and more devices are connected to the Internet there is a need to seek for methods to reduce the traffic. Fog computing was proposed to adding the computation and storage ability to the edge devices. The requests from the end device can be pre-processed or even responded at the edge device. In this work, the ability of Web Resource Caching is added to the edge device to serve as a caching proxy server. In order to obtain more caching storage, the end devices are also exploits to provide some caching space. As the whole caching storage is enlarged, large resource like media files are able to be cached near to the end devices once any of the end devices in the same network just recently requested it. The result shows that the proposed architecture has better downloading latency compares to the single caching proxy approach.

A mental disorder early warning approach by observing depression symptom in social diary.

With the advances of information technology, there are increasing researches aiming at assisting depression diagnosis and treatment. In most of them the user is necessarily actively joining the diagnosis and treatment program while he has perceived mental disorder himself. In order to early prevent the mental disorder, in this paper we propose an early warning mechanism that observes and mines user diary published on social network platform, and generates a score of getting mental disordered or depressed. If the score is large than a threshold, the system can notify the user and his friends on the social network to take care about the friend. We have conducted experiments to evaluate the proposed approach, and the results show that the proposed approach is effective.

An agent-based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

ummary With the advances of cloud computing, business and scientific-oriented jobs with certain workflows are increasingly migrated to and run on a variety of cloud environments. These jobs are often with the property of deadline constraint and have to be completed within limited time. Therefore, to schedule a job with workflow (short for workflow) with deadline constraint is increasingly becoming a crucial research issue. In this paper, we, based on previous work, propose an agent-based workflow scheduling mechanism to schedule workflows that are with deadline constraint into federated cloud environment. Design and Methods We add a workflow agent into the original framework to schedule the deadline-constraint workflow. The workflow agent can smoothly schedule workflows to the cloud system according to their required resource and automatically monitor their execution. In order to accurately predict the execution time of each task to meet deadline constraint on certain VM with given resource, we inherit the use of rough set theory to estimate execution time of task in our previous work. Result and Discussion A heuristic algorithm that is embedded into the workflow agent is also proposed because the problem had been shown to be NP-complete. The mechanism also adopts dynamic job dispatching method to reduce the usage of VM and to improve the resource utilization. We also conducted experiments to evaluate the efficiency and effectiveness. Conclusion The experimental results show that the prediction time is very close to the real execution time and can efficiently schedule multiple scientific workflows to meet the deadline constraints simultaneously.

VM instance selection for deadline constraint job on agent-based interconnected cloud

Abstract In recent years, users have been buying computing resources, such as VM instances, from the cloud resource providers. However, every cloud has its pricing models, solutions, and interfaces. Without a united interface, it is hard to manage across different clouds. To make things worse, it is even hard to select proper VM instances among multiple cloud resource providers when the resources are not sufficient. This paper proposes an interconnected cloud of selecting VM instances based on the job’s deadline constraint. To make the system function, five agents are created for different purposes: a System Monitoring Agent, a Job Dispatching Agent, an Instance Group Managing Agent, an Instance Group Administrating Agent, and a Job Executing Agent. This paper also presents two decision algorithms, the Job Dispatching Algorithm and the Instance Group Invocation Algorithm, based on the Rough Set Theory, the jobs’ deadline constraint, and the computing resource of the VM instances. The proposed system was deployed and evaluated on the real machines. The result shows not only the VM instances on different machines are interconnected, but also the algorithms successfully help manage the interconnected cloud’s computing resources according to the submitted jobs with deadline constraints.

Implementing a Workflow Agent on Federated Cloud

Workflow scheduling is the way to schedule the complex scientific workflow. In a cloud platform, the resources can be utilized more effectively and efficiently by scheduling the workflow properly. In this paper, an agent-based workflow scheduling mechanism on cloud environment is proposed. We implement a set of agents that can federate multiple clouds and automatically monitor the entire system, and manage the job if its a workflow job. We also implement a dynamic job dispatching algorithm to reduce the VM usage amount and improve the resource utilization.